





"""simple docstring"""





import time
from dataclasses import dataclass
from multiprocessing import Pool
from unittest import TestCase
from unittest.mock import patch

import multiprocess
import numpy as np
import pytest

from datasets.utils.py_utils import (
    NestedDataStructure,
    asdict,
    iflatmap_unordered,
    map_nested,
    temp_seed,
    temporary_assignment,
    zip_dict,
)

from .utils import require_tf, require_torch


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):  # picklable for multiprocessing







      '''simple docstring'''




      return x.sum()


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]     ):  # picklable for multiprocessing







      '''simple docstring'''




      return i + 1

@dataclass
class 						lowercase   :
      _SCREAMING_SNAKE_CASE							      = 42
      _SCREAMING_SNAKE_CASE							      = 42







class 						lowercase   (   _UpperCAmelCase   ):





      def        _snake_case     (			self					)						->	Any:
            lowerCAmelCase    		=			{}
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			1
            lowerCAmelCase    		=			[1, 2]
            lowerCAmelCase    		=			{"""a""": 1, """b""": 2}
            lowerCAmelCase    		=			{"""a""": [1, 2], """b""": [3, 4]}
            lowerCAmelCase    		=			{"""a""": {"""1""": 1}, """b""": 2}
            lowerCAmelCase    		=			{"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
            lowerCAmelCase    		=			{}
            lowerCAmelCase    		=			[]
            lowerCAmelCase    		=			2
            lowerCAmelCase    		=			[2, 3]
            lowerCAmelCase    		=			{"""a""": 2, """b""": 3}
            lowerCAmelCase    		=			{"""a""": [2, 3], """b""": [4, 5]}
            lowerCAmelCase    		=			{"""a""": {"""1""": 2}, """b""": 3}
            lowerCAmelCase    		=			{"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase					) ,		lowercase					)

            lowerCAmelCase    		=			2
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		num_proc=lowercase					) ,		lowercase					)

            lowerCAmelCase    		=			{"""a""": np.eye(2					), """b""": np.zeros(3					), """c""": np.ones(2					)}
            lowerCAmelCase    		=			{"""a""": 2, """b""": 0, """c""": 2}
            lowerCAmelCase    		=			{
                """a""": np.eye(2					).astype(lowercase					),
                """b""": np.zeros(3					).astype(lowercase					),
                """c""": np.ones(2					).astype(lowercase					),
            }
            self.assertEqual(map_nested(lowercase ,		lowercase ,		map_numpy=lowercase					) ,		lowercase					)
            self.assertEqual(
                {k: v.tolist() for k, v in map_nested(lowercase ,		lowercase ,		map_numpy=lowercase					).items()} ,		{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,		)
            self.assertEqual(map_nested(lowercase ,		lowercase ,		map_numpy=lowercase ,		num_proc=lowercase					) ,		lowercase					)
            self.assertEqual(
                {k: v.tolist() for k, v in map_nested(lowercase ,		lowercase ,		map_numpy=lowercase ,		num_proc=lowercase					).items()} ,		{k: v.tolist() for k, v in expected_map_nested_sna_int.items()} ,		)
            with self.assertRaises(lowercase					):  # can't pickle a local lambda
                  map_nested(lambda lowercase					: x + 1 ,		lowercase ,		num_proc=lowercase					)





      def        _snake_case     (			self					)						->	List[str]:
            lowerCAmelCase    		=			{"""a""": 1, """b""": 2}
            lowerCAmelCase    		=			{"""a""": 3, """b""": 4}
            lowerCAmelCase    		=			{"""a""": 5, """b""": 6}
            lowerCAmelCase    		=			sorted([("""a""", (1, 3, 5)), ("""b""", (2, 4, 6))]					)
            self.assertEqual(sorted(zip_dict(lowercase ,		lowercase ,		lowercase					)					) ,		lowercase					)





      def        _snake_case     (			self					)						->	Optional[Any]:

            class 						lowercase   :
                  _SCREAMING_SNAKE_CASE							      = 'bar'







            lowerCAmelCase    		=			Foo()
            self.assertEqual(foo.my_attr ,		"""bar"""					)
            with temporary_assignment(lowercase ,		"""my_attr""" ,		"""BAR"""					):
                  self.assertEqual(foo.my_attr ,		"""BAR"""					)
            self.assertEqual(foo.my_attr ,		"""bar"""					)







@pytest.mark.parametrize(
    """iterable_length, num_proc, expected_num_proc"""				,		[
        (1, None, 1),
        (1, 1, 1),
        (2, None, 1),
        (2, 1, 1),
        (2, 2, 1),
        (2, 3, 1),
        (3, 2, 1),
        (16, 16, 16),
        (16, 17, 16),
        (17, 16, 16),
    ]				,		)
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		str				,		SCREAMING_SNAKE_CASE	:		Optional[Any]     ):







      '''simple docstring'''




      with patch("""datasets.utils.py_utils._single_map_nested"""     ) as mock_single_map_nested, patch(
          """datasets.parallel.parallel.Pool"""     ) as mock_multiprocessing_pool:
            lowerCAmelCase    		=			{F'{i}': i for i in range(SCREAMING_SNAKE_CASE     )}
            lowerCAmelCase    		=			map_nested(lambda SCREAMING_SNAKE_CASE     : x + 10				,		SCREAMING_SNAKE_CASE				,		num_proc=SCREAMING_SNAKE_CASE				,		parallel_min_length=16     )
            if expected_num_proc == 1:
                  assert mock_single_map_nested.called
                  assert not mock_multiprocessing_pool.called
            else:
                  assert not mock_single_map_nested.called
                  assert mock_multiprocessing_pool.called
                  assert mock_multiprocessing_pool.call_args[0][0] == expected_num_proc

class 						lowercase   (   _UpperCAmelCase   ):





      @require_tf
      def        _snake_case     (			self					)						->	Optional[int]:
            import tensorflow as tf
            from tensorflow.keras import layers

            lowerCAmelCase    		=			layers.Dense(2					)

            def gen_random_output():
                  lowerCAmelCase    		=			tf.random.uniform((1, 3)					)
                  return model(lowercase					).numpy()

            with temp_seed(42 ,		set_tensorflow=lowercase					):
                  lowerCAmelCase    		=			gen_random_output()
            with temp_seed(42 ,		set_tensorflow=lowercase					):
                  lowerCAmelCase    		=			gen_random_output()
            lowerCAmelCase    		=			gen_random_output()

            np.testing.assert_equal(lowercase ,		lowercase					)
            self.assertGreater(np.abs(outa - outa					).sum() ,		0					)





      @require_torch
      def        _snake_case     (			self					)						->	Tuple:
            import torch

            def gen_random_output():
                  lowerCAmelCase    		=			torch.nn.Linear(3 ,		2					)
                  lowerCAmelCase    		=			torch.rand(1 ,		3					)
                  return model(lowercase					).detach().numpy()

            with temp_seed(42 ,		set_pytorch=lowercase					):
                  lowerCAmelCase    		=			gen_random_output()
            with temp_seed(42 ,		set_pytorch=lowercase					):
                  lowerCAmelCase    		=			gen_random_output()
            lowerCAmelCase    		=			gen_random_output()

            np.testing.assert_equal(lowercase ,		lowercase					)
            self.assertGreater(np.abs(outa - outa					).sum() ,		0					)





      def        _snake_case     (			self					)						->	Union[str, Any]:
            def gen_random_output():
                  return np.random.rand(1 ,		3					)

            with temp_seed(42					):
                  lowerCAmelCase    		=			gen_random_output()
            with temp_seed(42					):
                  lowerCAmelCase    		=			gen_random_output()
            lowerCAmelCase    		=			gen_random_output()

            np.testing.assert_equal(lowercase ,		lowercase					)
            self.assertGreater(np.abs(outa - outa					).sum() ,		0					)







@pytest.mark.parametrize("""input_data"""				,		[{}]     )
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      lowerCAmelCase    		=			NestedDataStructure(SCREAMING_SNAKE_CASE     ).data
      assert output_data == input_data


@pytest.mark.parametrize(
    """data, expected_output"""				,		[
        ({}, []),
        ([], []),
        ("""foo""", ["""foo"""]),
        (["""foo""", """bar"""], ["""foo""", """bar"""]),
        ([["""foo""", """bar"""]], ["""foo""", """bar"""]),
        ([[["""foo"""], ["""bar"""]]], ["""foo""", """bar"""]),
        ([[["""foo"""], """bar"""]], ["""foo""", """bar"""]),
        ({"""a""": 1, """b""": 2}, [1, 2]),
        ({"""a""": [1, 2], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[1, 2]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[[3], [4]]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [[3, 4]]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, 4]}, [1, 2, 3, 4]),
        ({"""a""": [[[1], [2]]], """b""": [3, [4]]}, [1, 2, 3, 4]),
        ({"""a""": {"""1""": 1}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": 2}, [1, 2]),
        ({"""a""": {"""1""": [1]}, """b""": [2]}, [1, 2]),
    ]				,		)
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[int]				,		SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			NestedDataStructure(SCREAMING_SNAKE_CASE     ).flatten()
      assert output == expected_output


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			A(x=1				,		y="""foobar"""     )
      lowerCAmelCase    		=			{"""x""": 1, """y""": """foobar"""}
      assert asdict(SCREAMING_SNAKE_CASE     ) == expected_output

      lowerCAmelCase    		=			{"""a""": {"""b""": A(x=10				,		y="""foo"""     )}, """c""": [A(x=20				,		y="""bar"""     )]}
      lowerCAmelCase    		=			{"""a""": {"""b""": {"""x""": 10, """y""": """foo"""}}, """c""": [{"""x""": 20, """y""": """bar"""}]}
      assert asdict(SCREAMING_SNAKE_CASE     ) == expected_output

      with pytest.raises(SCREAMING_SNAKE_CASE     ):
            asdict([1, A(x=10				,		y="""foo"""     )]     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		str     ):







      '''simple docstring'''




      return text.split()


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      yield (time.time(), content)
      time.sleep(2     )
      yield (time.time(), content)






def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      with Pool(2     ) as pool:
            lowerCAmelCase    		=			list(iflatmap_unordered(SCREAMING_SNAKE_CASE				,		_split_text				,		kwargs_iterable=[{"""text""": """hello there"""}] * 10     )     )
            assert out.count("""hello"""     ) == 10
            assert out.count("""there"""     ) == 10
            assert len(SCREAMING_SNAKE_CASE     ) == 20

      # check multiprocess from pathos (uses dill for pickling)
      with multiprocess.Pool(2     ) as pool:
            lowerCAmelCase    		=			list(iflatmap_unordered(SCREAMING_SNAKE_CASE				,		_split_text				,		kwargs_iterable=[{"""text""": """hello there"""}] * 10     )     )
            assert out.count("""hello"""     ) == 10
            assert out.count("""there"""     ) == 10
            assert len(SCREAMING_SNAKE_CASE     ) == 20

      # check that we get items as fast as possible
      with Pool(2     ) as pool:
            lowerCAmelCase    		=			[]
            for yield_time, content in iflatmap_unordered(
                SCREAMING_SNAKE_CASE				,		_aseconds_generator_of_aitems_with_timing				,		kwargs_iterable=[{"""content""": """a"""}, {"""content""": """b"""}]     ):
                  assert yield_time < time.time() + 0.1, "we should each item directly after it was yielded"
                  out.append(SCREAMING_SNAKE_CASE     )
            assert out.count("""a"""     ) == 2
            assert out.count("""b"""     ) == 2
            assert len(SCREAMING_SNAKE_CASE     ) == 4













"""simple docstring"""





import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json",
    },
    "merges_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "allenai/led-base-16384": "https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json",
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "allenai/led-base-16384": 16_384,
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = LEDTokenizer
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']





      def __init__(			self ,		lowercase=None ,		lowercase=None ,		lowercase=None ,		lowercase="replace" ,		lowercase="<s>" ,		lowercase="</s>" ,		lowercase="</s>" ,		lowercase="<s>" ,		lowercase="<unk>" ,		lowercase="<pad>" ,		lowercase="<mask>" ,		lowercase=False ,		lowercase=True ,		**lowercase ,		)						->	Any:
            super().__init__(
                lowercase ,		lowercase ,		tokenizer_file=lowercase ,		errors=lowercase ,		bos_token=lowercase ,		eos_token=lowercase ,		sep_token=lowercase ,		cls_token=lowercase ,		unk_token=lowercase ,		pad_token=lowercase ,		mask_token=lowercase ,		add_prefix_space=lowercase ,		trim_offsets=lowercase ,		**lowercase ,		)

            lowerCAmelCase    		=			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()					)
            if pre_tok_state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                  lowerCAmelCase    		=			getattr(lowercase ,		pre_tok_state.pop("""type"""					)					)
                  lowerCAmelCase    		=			add_prefix_space
                  lowerCAmelCase    		=			pre_tok_class(**lowercase					)

            lowerCAmelCase    		=			add_prefix_space

            # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
            lowerCAmelCase    		=			"""post_processor"""
            lowerCAmelCase    		=			getattr(self.backend_tokenizer ,		lowercase ,		lowercase					)
            if tokenizer_component_instance:
                  lowerCAmelCase    		=			json.loads(tokenizer_component_instance.__getstate__()					)

                  # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                  if "sep" in state:
                        lowerCAmelCase    		=			tuple(state["""sep"""]					)
                  if "cls" in state:
                        lowerCAmelCase    		=			tuple(state["""cls"""]					)

                  lowerCAmelCase    		=			False

                  if state.get("""add_prefix_space""" ,		lowercase					) != add_prefix_space:
                        lowerCAmelCase    		=			add_prefix_space
                        lowerCAmelCase    		=			True

                  if state.get("""trim_offsets""" ,		lowercase					) != trim_offsets:
                        lowerCAmelCase    		=			trim_offsets
                        lowerCAmelCase    		=			True

                  if changes_to_apply:
                        lowerCAmelCase    		=			getattr(lowercase ,		state.pop("""type"""					)					)
                        lowerCAmelCase    		=			component_class(**lowercase					)
                        setattr(self.backend_tokenizer ,		lowercase ,		lowercase					)





      @property
      # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
      def        _snake_case     (			self					)						->	str:
            if self._mask_token is None:
                  if self.verbose:
                        logger.error("""Using mask_token, but it is not set yet."""					)
                  return None
            return str(self._mask_token					)





      @mask_token.setter
      def        _snake_case     (			self ,		lowercase					)						->	Optional[int]:
            lowerCAmelCase    		=			AddedToken(lowercase ,		lstrip=lowercase ,		rstrip=lowercase					) if isinstance(lowercase ,		lowercase					) else value
            lowerCAmelCase    		=			value





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._batch_encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		*lowercase ,		**lowercase					)						->	BatchEncoding:
            lowerCAmelCase    		=			kwargs.get("""is_split_into_words""" ,		lowercase					)

            if is_split_into_words and not self.add_prefix_space:
                  raise ValueError(
                      f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                      """to use it with pretokenized inputs."""					)

            return super()._encode_plus(*lowercase ,		**lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	Tuple[str]:
            lowerCAmelCase    		=			self._tokenizer.model.save(lowercase ,		name=lowercase					)
            return tuple(lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase=None					)						->	Any:
            lowerCAmelCase    		=			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
            if token_ids_a is None:
                  return output

            return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None					)						->	List[int]:
            lowerCAmelCase    		=			[self.sep_token_id]
            lowerCAmelCase    		=			[self.cls_token_id]

            if token_ids_a is None:
                  return len(cls + token_ids_a + sep					) * [0]
            return len(cls + token_ids_a + sep + sep + token_ids_a + sep					) * [0]





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		lowercase = PaddingStrategy.DO_NOT_PAD ,		lowercase = None ,		lowercase = None ,		)						->	dict:
            lowerCAmelCase    		=			super()._pad(
                encoded_inputs=lowercase ,		max_length=lowercase ,		padding_strategy=lowercase ,		pad_to_multiple_of=lowercase ,		return_attention_mask=lowercase ,		)

            # Load from model defaults
            if return_attention_mask is None:
                  lowerCAmelCase    		=			"""attention_mask""" in self.model_input_names

            if return_attention_mask and "global_attention_mask" in encoded_inputs:
                  lowerCAmelCase    		=			encoded_inputs[self.model_input_names[0]]
                  # `global_attention_mask` need to have the same length as other (sequential) inputs.
                  lowerCAmelCase    		=			len(encoded_inputs["""global_attention_mask"""]					) != len(lowercase					)

                  if needs_to_be_padded:
                        lowerCAmelCase    		=			len(lowercase					) - len(encoded_inputs["""global_attention_mask"""]					)

                        if self.padding_side == "right":
                              # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
                              lowerCAmelCase    		=			(
                                  encoded_inputs["""global_attention_mask"""] + [-1] * difference
                              )
                        elif self.padding_side == "left":
                              lowerCAmelCase    		=			[-1] * difference + encoded_inputs[
                                  """global_attention_mask"""
                              ]
                        else:
                              raise ValueError("""Invalid padding strategy:""" + str(self.padding_side					)					)

            return encoded_inputs






